- The problem with articles like this one is, they give ways to become efficient at handling more addiction, at the individual level. Nothing for others part of this, companies developing the software and organizations employing these tools.
Summary of the addiction management tips from the article.
1. Time-box your AI coding sessions with a clear goal and a hard end time.
2. Separate exploration (testing ideas) from execution (shipping code) to avoid losing focus.
3. Prioritize sleep, hard stops, and actual recovery as essential maintenance, not just wellness.
4. Invest in structured training to move from basic usage to advanced multi-agent workflows.
5. Personalize your AI workflow to fit your needs while actively avoiding common anti-patterns.
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When a developer stops writing code and starts using Claude to handle multiple projects at once, they are essentially managing the outcomes.
They have become 10x engineering managers. The context strain and emotional strain is overwhelming.
- Just started at a company and the amount of irresponsible AI use is appalling. I asked an employee whose job involves AI adoption/training how large their diffs are for pull requests. They told me that their diffs are "As much as the model can produce given its reasoning level".
In the end, this is going to create unmaintainable code that no one understands. It also discourages reviewing the code because no dev can meaningfully review 1000s of lines of code in a day while also accomplishing their tasks.
NOTE: I am still pro AI, just like I am pro heavy machinery. I just don't want people to cut off their legs...
- why do you care how large the diffs are. isnt there any other way to measure if ai is producing value?
- Not OP, but I think it's not about producing value now, but how much it will cost in the long term. If you have unmaintaable code that is N times larger than a hand-written codebase, what is the cost to be?
- Its about the team being able to review the code to tell if its slop or not. It's hard to meaningfully review huge changes to a codebase for one PR. Just imagine if there are 5 PRs a day with 1000+ insertions. It leads to the production codebase being somewhat of a black box imo
- > AI is keeping engineers at their desks longer, not freeing them up. Random rewards, dopamine hits, and no natural stopping points create a loop comparable to casino gambling.
> The fix is deliberate habits, not restricted tools. Time-box sessions, separate exploration from execution, and treat recovery as maintenance.
Getting tired of AI slop telling me about AI.
- Or "paying the price" literally. I'm returning after a small break and into new GitHub Copilot prices. A simple question / request / analysis on Sonnet - that will be 29 cents, please. I can't imagine how much it will cost to do actual development with it.
- What type of AI coding? I do not have the attention span to sit there and let these LLMs just churn away. I have to be the one doing the typing or nothing will be accomplished. I tried playing Claude Code and Codex a bit. While impressive in their outputs (at times), I just find the workflow to be so dissatisfying.
One other aspect of LLMs that I do not enjoy when it comes to development is the fact that LLMs minimize my contributions. I do not feel like I can take credit for anything I create if I technically did not create it.
However, I absolutely adore LLMs for learning new concepts and for troubleshooting. To me, that is where they shine the brightest.
25 points by sefrost 2 hours ago | 11 comments